TEXTure: Text-Guided Texturing of 3D Shapes

By Elad Richardson, Gal Metzer, Yuval Alaluf, Raja Giryes, Daniel Cohen-Or. International Conference on Computer Graphics and Interactive Techniques, 2023

384 citations recorded by the source corpus.

In this paper, we present TEXTure, a novel method for text-guided generation, editing, and transfer of textures for 3D shapes. Leveraging a pretrained depth-to-image diffusion model, TEXTure applies an iterative scheme that paints a 3D model from different viewpoints. Yet, while depth-to-image models can create plausible textures from a single viewpoint, the stochastic nature of the generation process can cause many inconsistencies when texturing an entire 3D object. To tackle these problems, we dynamically define a trimap partitioning of the rendered image into three progression states, and present a novel elaborated diffusion sampling process that uses this trimap representation to generate seamless textures from different views. We then show that one can transfer the generated texture maps to new 3D geometries without requiring explicit surface-to-surface mapping, as well as extract semantic textures from a set of images without requiring any explicit reconstruction. Finally, we show that TEXTure can be used to not only generate new textures but also edit and refine existing textures using either a text prompt or user-provided scribbles. We demonstrate that our TEXTuring method excels at generating, transferring, and editing textures through extensive evaluation, and further close the gap between 2D image generation and 3D texturing. Code is available via our project page: https://texturepaper.github.io/TEXTurePaper/.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/5affc264027ec9fc95709c059613333b946cf271

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Citations in the corpus

One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization (2023, methodology): “In addition to optimization-based 3D shape generation, some works train a 3D generative model but leverage the embedding space of CLIP [8, 39, 67], and some works focus on generating textures or materials for input meshes using 2D models’ prior [47, 76, 7, 46, 63].”
DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation (2023, methodology): “Following Richardson et al. (2023), we exclude the pixels with a small camera space z-direction normal to avoid unstable projection at mesh boundaries.”
Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors (2023, methodology): “The developed methods improved the underlying 3D representation [43, 11, 84] and 3D consistency of the supervision [46, 77]; explored task-specific priors [28, 33, 70] and additional controls [54].”
Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models (2023, methodology): “Related methods refine existing 3D input through text guidance in a similar fashion [46, 10, 87, 62].”
CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assets (2024, positive): “Utilizing this dataset, we developed a multi-view Material Diffusion to synthesize textures with a significantly speed-up over existing methods, which are then accurately mapped onto the geometries’ UV space in a way similar to TEXTure [Richardson et al. 2023].”
One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion (2023, background): “Some works also concentrate on creating textures or materials for input meshes, utilizing the priors of 2D models [5, 55].”
Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation (2025, neutral): “We compare Hunyuan3D-Paint with the following texture generation methods, including TEXTure [73], Text2Tex [9], SyncMVD [59], Paint3D [111], and TexPainter [113].”